Intracranial EEG for Neuronal Oscillatory Contingency during Cognitive Tasks

描述(申请人提供)：涉及运动和感觉系统的脑机接口(BCI)在恢复功能和改善神经系统疾病患者的生活方面取得了成功。这些设备，从帕金森氏病的深部脑刺激器到更复杂的能够重现运动功能的设备，随着最近的技术进步和对潜在神经电路的更多了解，正变得越来越可用。这些设备没有涉及的一个主要领域是高级认知功能，如记忆和执行计划。在这里，我们建议使用从植入药物难治性癫痫患者的硬膜下和深部电极捕获的脑皮质电信号，将BCI的作用扩展到认知域。这一建议的中心假设是，记忆存储和回忆背后的神经元活动模式可以用来调整认知任务中的刺激呈现，以增强学习。我们建议开发一种脑机接口，当患者参与自由回忆任务时，它可以实时测量和分析颅内神经活动，这是一种测量一个人编码和提取情节记忆能力的标准方法。使用机器学习算法，我们将识别导致最佳记忆形成的电生理大脑活动的精确时空模式。我们将为每个患者分别计算这些模式。因此，我们的系统将适应每个人的大脑活动。为了关闭患者大脑和我们系统之间的功能环路，我们将利用这些自然发生的最佳时空模式来形成记忆，并在它们出现时触发刺激呈现。动物研究表明，通过根据海马体的振荡状态提供刺激，可以提高学习速度。我们希望证明，在存在这些神经活动的最佳时空模式的情况下，条件性刺激呈现也将改善患者的记忆存储和回忆。这样的论证将在这些活动模式和记忆编码之间建立一种因果关系，而不是相互关系。此外，我们的研究将把脑机接口的领域扩展到认知和记忆领域，并为一系列可以增强人类各种认知功能的脑机接口系统奠定基础。 与公共健康相关：脑机接口(BCI)是通过实时测量和分析神经活动直接与大脑交互的机器。近年来，BCI在帕金森氏病、严重抑郁症和癫痫患者的康复中发挥了不可或缺的作用。到目前为止，BCI尚未解决的一个关键领域是高级认知功能，包括记忆--这是包括阿尔茨海默病在内的许多神经疾病的主要缺陷之一。在这里，我们提出了一种新的脑机接口，用于改善记忆受损和认知正常的人的记忆表现。

DESCRIPTION (provided by applicant): Brain computer interfaces (BCI) involving motor and sensory systems have been successful in restoring function and in improving the lives of patients with neurological diseases. These devices, ranging from deep brain stimulators for Parkinson's disease to more involved devices capable of recapitulating motor function, are becoming increasingly available with recent technological advances and with a greater understanding of underlying neural circuitry. A major area not addressed by these devices is that of higher cognitive functions, such as memory and executive planning. Here, we propose to use intracranial electrocorticographic signals, captured from subdural and depth electrodes implanted in patients with pharmacologically intractable epilepsy, to extend the role of BCIs to the cognitive domain. The central hypothesis of this proposal is that the patterns of neuronal activity that underlie memory storage and recall can be used to adjust stimulus presentation in a cognitive task to augment learning. We propose to develop a BCI that measures and analyzes intracranial neural activity in real time as patients engage in a free recall task, a standard method of measuring one's ability to encode and retrieve episodic memories. Using machine-learning algorithms, we will identify the precise spatiotemporal patterns of electrophysiological brain activity that lead to optimal memory formation. We will compute these patterns separately for each patient. Our system will thus adapt to each individual's brain activity. To close the functional loop between the patient's brain and our system, we will take advantage of these naturally occurring optimal spatiotemporal patterns for memory formation and trigger stimulus presentation on their presence. Animal studies have shown that by presenting stimuli contingent on the oscillatory state of the hippocampus, learning rates can be improved. We hope to demonstrate that conditioning stimulus presentation on the presence of these optimal spatiotemporal patterns of neural activity will improve both memory storage and recall in our patients as well. Such a demonstration will establish a causative, rather than correlational, relationship between these patterns of activity and memory encoding. Furthermore, our research will extend the domain of BCIs to the realm of cognition and memory, and lay the groundwork for a range of BCI systems that can enhance a wide variety of human cognitive functions. PUBLIC HEALTH RELEVANCE: Brain computer interfaces (BCIs) are machines that directly interface with the brain by measuring and analyzing neural activity in real time. Recent BCIs have played an integral role in rehabilitating patients suffering from Parkinson's disease, severe depression, and epilepsy. One critical area not addressed by BCIs to date is that of higher cognitive functions, including memory -- one of the major deficits in a number of neural diseases, including Alzheimer's disease. Here we propose a novel BCI for improving memory performance both in memory-impaired and cognitively normal individuals.